Compression is ubiquitous in consumer electronics. Modern compression algorithms exploit the limitations of human hearing and vision to offer compression ratios from 4:1 for speech and more than 30:1 ...

Finding efficient ways to compress and decompress data is more important than ever. Compressed data takes up less space and requires less time and network bandwidth to transfer. In cloud service code, ...

“Irregular applications, such as graph analytics and sparse linear algebra, exhibit frequent indirect, data-dependent accesses to single or short sequences of elements that cause high main memory ...

Efficient data compression and transmission are crucial in space missions due to restricted resources, such as bandwidth and storage capacity. This requires efficient data-compression methods that ...

Innovative compression algorithms can improve today�s high-speed data sampling. Compression algorithms have enabled many popular applications over the years. We cannot imagine sending e-mail ...

Information theory provides the fundamental framework for understanding and designing data compression algorithms. At its core lies the concept of entropy, a quantitative measure that reflects the ...

Many of today's embedded systems are providing more sophisticated solutions to a wide variety of applications and industries. With this increase in sophistication, there is a corresponding increase in ...

Optimizing data compression methods has become more critical than ever for cloud storage, data management, and streaming applications. Working with compressed data reduces network bandwidth, data ...

Compression is the science of making data representations smaller, in order to decrease the data's bandwidth and storage requirements. Compression applications are everywhere: in computers (WinZip and ...

Fuzzy transforms (F-transforms) are a class of fuzzy approximation methods that utilise fuzzy partitions to represent and reconstruct functions through a collection of weighted average values. By ...